The ability to monitor vehicular traffic is important for many business and governmental purposes. Video monitoring may enable effective coordinated management of roads, traffic signals and even parking facilities. For example, to coordinate usage within a single parking structure, among multiple parking structures, on-street parking, or combinations of these. Video monitoring may provide timely and accurate information to adjust traffic signals among multiple intersections to alleviate local congestion and smooth traffic flows. Video monitoring can be used off-road within parking structures to identify hidden or unknown parking spaces, parking space occupancy, assign parking space categories, detect illegally (hazardously) parked cars, reduce inefficient use of parking capacity, enable spillover accommodation, adjust lighting, monitor handicap space availability, etc.
Video monitoring of moving vehicular traffic may enable real-time management of traffic flows, for example by the timing of traffic lights or redirection of traffic lanes. The accumulation of data from video monitored vehicular traffic may provide a basis for the design or redesign of roadways and associated structures.
Existing video monitoring techniques detect a moving object by comparing a sample of the image containing the moving object with previously stored images of the area being viewed. The previously stored images of the area being viewed may be described by a statistical model of the viewed area. Those portions of the sampled image that do not fit with the statistical model may be identified as foreground regions, a process referred to as background subtraction. Existing background subtraction techniques are based on static images and are mainly designed to identify non-moving objects. As any system also needs to learn variety of conditions (like weather, shadow, lighting etc) and modifications in background in parallel to detecting the foreground, existing background methods tend to merge the stationary foreground objects into background considering them as modified background. When moving objects are involved, the existing algorithms slowly merge the categorization of a stationary foreground region, into being categorized as a background region. Such existing techniques do not keep track of an identified foreground region, which is presently stationary, as continuing to be a foreground region. Existing techniques also have limitations due to wind, rain, reflections or illumination changes. In applications such as real-time traffic flow management, merger of foreground with background could inadvertently result in directing vehicular traffic into a collision with a foreground object that was erroneously categorized as background.